The field of the invention is magnetic resonance imaging (“MRI”) systems and methods. More particularly, the invention relates to systems and methods for separating the signal contributions from two or more different species from signals acquired with and MRI system from a subject.
Magnetic resonance imaging (“MRI”) uses the nuclear magnetic resonance (“NMR”) phenomenon to produce images. When a substance such as human tissue is subjected to a uniform magnetic field (polarizing field B0), the individual magnetic moments of the nuclei in the tissue attempt to align with this polarizing field, but precess about it in random order at their characteristic Larmor frequency. If the substance, or tissue, is subjected to a magnetic field (excitation field B1) that is in the x-y plane and that is near the Larmor frequency, the net aligned moment, Mz, may be rotated, or “tipped,” into the x-y plane to produce a net transverse magnetic moment Mxy. A signal is emitted by the excited nuclei or “spins,” after the excitation signal B1 is terminated, and this signal may be received and processed to form an image.
When utilizing these “MR” signals to produce images, magnetic field gradients (Gx, Gy, and Gz) are employed. Typically, the region to be imaged is scanned by a sequence of measurement cycles in which these gradients vary according to the particular localization method being used. The resulting set of received MR signals are digitized and processed to reconstruct the image using one of many well known reconstruction techniques.
The measurement cycle used to acquire each MR signal is performed under the direction of a pulse sequence produced by a pulse sequencer. Clinically available MRI systems store a library of such pulse sequences that can be prescribed to meet the needs of many different clinical applications. Research MRI systems include a library of clinically-proven pulse sequences and they also enable the development of new pulse sequences.
The MR signals acquired with an MRI system are signal samples of the subject of the examination in Fourier space, or what is often referred to in the art as “k-space.” Each MR measurement cycle, or pulse sequence, typically samples a portion of k-space along a sampling trajectory characteristic of that pulse sequence. Most pulse sequences sample k-space in a raster scan-like pattern sometimes referred to as a “spin-warp,” a “Fourier,” a “rectilinear,” or a “Cartesian” scan. The spin-warp scan technique employs a variable amplitude phase encoding magnetic field gradient pulse prior to the acquisition of MR spin-echo signals to phase encode spatial information in the direction of this gradient. In a two-dimensional implementation (“2DFT”), for example, spatial information is encoded in one direction by applying a phase encoding gradient, Gy, along that direction, and then a spin-echo signal is acquired in the presence of a readout magnetic field gradient, Gx, in a direction orthogonal to the phase encoding direction. The readout gradient present during the spin-echo acquisition encodes spatial information in the orthogonal direction. In a typical 2DFT pulse sequence, the magnitude of the phase encoding gradient pulse, Gy, is incremented, ΔGy, in the sequence of measurement cycles, or “views” that are acquired during the scan to produce a set of k-space MR data from which an entire image can be reconstructed.
An image is reconstructed from the acquired k-space data by transforming the k-space data set to an image space data set. There are many different methods for performing this task and the method used is often determined by the technique used to acquire the k-space data. With a Cartesian grid of k-space data that results from a 2D or 3D spin-warp acquisition, for example, the most common reconstruction method used is an inverse Fourier transformation (“2DFT” or “3DFT”) along each of the 2 or 3 axes of the data set.
Conventional fat suppression or water-fat decomposition methods model fat as a single resonance frequency at approximately 3.5 ppm (210 Hz at a field strength of 1.5 Tesla, and 420 Hz at a magnetic field strength of 3.0 Tesla) away from the water resonance frequency. Exemplary methods of conventional fat suppression include spectral saturation (“FatSat”), and chemical-shift based multi-point Dixon methods. Multi-point water-fat separation methods have seen increasing use in routine clinical applications, following recent developments that make them more reliable, faster, and flexible. In general, these methods acquire signal from multiple echoes with different water-fat phase shifts, so that water and fat can be separated with the correction of B0 field inhomogeneities. Commonly, two or three echoes are acquired, which is sufficient for qualitative water-fat separation. When more echoes are collected, however, newer algorithms can simultaneously estimate a T2* decay map, thereby producing water and fat images that are corrected for T2* decay effects.
Recently, a new method known as iterative decomposition of water and fat with echo asymmetry and least squares estimation (“IDEAL”) was developed for imaging spin species such as fat and water. As described in U.S. Pat. No. 6,856,134, the IDEAL method employs pulse sequences to acquire multiple images at different echo times (“TE”) and an iterative, linear least squares approach to estimate the separate water and fat signal components. However, this method also models the fat signal as having one resonance frequency, as do all other multi-point Dixon methods.
The multi-point water-fat separation methods, from the early Dixon methods to more recently IDEAL algorithms, all must address the intrinsic challenge of water-fat ambiguity. This ambiguity problem arises due to the fact that the signal behavior of two chemical species with a single NMR spectrum, but at different chemical shifts, may appear identical in the presence of B0 inhomogeneities. In this situation, the signal from a voxel containing substantially only one species can be identical for two possible scenarios. First, where the voxel contains a first species, such as water, with a given B0 off-resonance value, and second, where the voxel contains a second species, such as fat, with a different B0 off-resonance value. For example, with water and fat, a voxel containing only fat “looks” just like a voxel containing only water that is off-resonance by 210 hertz (“Hz”) at a B0 field strength of 1.5 Tesla (“T”). This ambiguity is the fundamental challenge of chemical shift based chemical species separation, and for water and fat is, therefore, commonly referred to as the “water-fat ambiguity.” Such ambiguities often result in water-fat swaps, in which a voxel containing, for example, water is mischaracterized as containing fat.
The challenge of water-fat ambiguity is commonly addressed by assuming a slowly and smoothly varying B0 field inhomogeneity map, or “field map,” without abrupt discontinuities, or “jumps,” in the field map that would occur when there is a water-fat swap. Previous multi-echo water-fat separation methods attempt to resolve the water-fat ambiguity by enforcing field map smoothness. However, these algorithms are typically based on variations of region growing algorithms, and are similar in principle to two-dimensional phase unwrapping methods, which are well known to be error prone and sensitive to noise and the physical characteristics of the object. Despite a number of sophisticated algorithms that have been developed by various groups, accurate field map estimation for extremely robust water-fat separation is very challenging.
In particular, when pulse sequences that acquire signals from multiple echoes per each repetition time (“TR”) period are used, the minimum echo time TE increment for a given desired resolution is increased, effectively reducing the spectral field-of-view (“FOV”) in which the field map can be uniquely determined. As a result, it is more challenging to design a robust field map algorithm for acquisitions with larger phase shifts, which occur with longer echo time increments, such as is needed with high resolution imaging, or at higher B0 field strengths, such as 3 T and above. These limitations lead to compromises in available imaging choices.